Inside the Gig Economy

By Tia Koonse, Saba Waheed

Alex Rosenblat’s Uberland: How Algorithms are Rewriting the Rules of Work examines the ride-hailing company Uber and the processes it uses to conduct its business operations. Rosenblat surveys the experiences of Uber’s drivers, the company’s growth model, and its high-profile controversies. She thoroughly and compellingly dismantles Uber’s deceptive sales pitch—that you can become an entrepreneur with nothing more than your own car—with well-sourced quotes, advertisements, and screenshots.

Some chapters of Uberland are essentially scrapbooks of how Uber controls driver choices through its algorithm, unilaterally decides rates of pay and rider interaction rules, and penalizes drivers for underperformance or noncompliance. One of Rosenblat’s chief concerns is driver experience, and her findings are based on 125 interviews with drivers, field observations from 400 rides between 2014 and 2018 in 25 cities in the United States and Canada, and observations from online driver forums with nearly 300,000 total members.

Rosenblat shines when she relies on the voices of drivers. She begins with Freddy, who works full time at a fast food restaurant, drives two hours a day for Uber, works through his vacation, and yet still has to sleep in his car. Later, Rosenblat introduces Cole, an Atlantan whose harrowing experience with a dangerous rider reveals Uber’s inadequate safety protocols for both drivers and passengers. Although some observers appreciate so-called transportation network companies such as Uber and Lyft for providing flexibility for drivers and relatively easy entry to employment, Uber’s persistent failure to live up to its promises pervades these pages.

Uberland also meticulously documents Uber’s insistence that it is a cutting-edge Silicon Valley tech company—not a transportation company, and certainly not an employer. Rosenblat presents a litany of ways in which the Uber app’s algorithm undermines the supposed neutrality of a platform that connects drivers and riders. In fact, the algorithm compels much of the drivers’ behavior: it deactivates them for declining passengers; pushes them to drive at particular times and places; sends them on nonoptimal routes in order to gather data about ridership for the company; and even penalizes drivers who attempt to maximize wages by adroit use of the algorithm. Rosenblat convincingly accuses Uber of failing to be an “honest broker” of its data; the company filters and manages data through its opaque algorithm, and rarely to the benefit of its drivers. Most importantly, the company unilaterally controls and changes pay rates. Drivers Rosenblat interviewed had Orwellian nicknames for this practice, such as “Uber math,” for when drivers don’t earn what they expect.

The algorithm compels much of the drivers’ behavior: it deactivates them for declining passengers; pushes them to drive at particular times and places; sends them on nonoptimal routes in order to gather data about ridership for the company; and even penalizes drivers who attempt to maximize wages by adroit use of the algorithm.

Uberland also details Uber’s growth model, which hinges on ignoring municipal regulation to enter new cities without heeding relevant taxi, registration, or background check regulations. Cities that have attempted to ban or restrict Uber struggle to enforce these restrictions. Rosenblat describes Uber’s high-profile and duplicitous practices in some cities, involving a tool called “Greyball,” to prevent law enforcement from finding cars that were operating in jurisdictions that were attempting to outlaw the service. In other places, Uber simply superseded municipal jurisdictions by successfully lobbying 41 state legislatures to preempt local, city-level regulation.

Rosenblat spends some time documenting opposition to Uber, including the European Union’s highest court’s insistence on regulating Uber as a cab company, the refusal of many women in tech nonprofits to accept Uber money following revelations of pervasive sexual harassment at the company, and the 200,000 strong #DeleteUber campaign. But Rosenblat’s research unfortunately predated the massive driver strikes across 10 US cities in 2019 protesting such things as unilateral cuts in pay and driver deactivations, among other problems. It also predated California’s recent high court decision, as well as the state legislature’s passage of Assembly Bill 5, confirming Uber’s status as an employer—an employer subject to minimum wage, antidiscrimination, and antiretaliation laws. Given such absences, Rosenblat’s story offers a misguided—and deflating—narrative of Uber’s inevitability, invincibility, and ubiquity.

Rosenblat describes Uber’s high-profile and duplicitous practices in some cities, involving a tool called “Greyball,” to prevent law enforcement from finding cars that were operating in jurisdictions that were attempting to outlaw the service.

To understand Uber’s corporate model and culture, Rosenblat briefly mentions two other Silicon Valley icons, Facebook and Google, that along with Uber assume and assert what the author calls their “technological exceptionalism.” This belief permits these companies to “disrupt” necessary infrastructure (transportation, the internet) with an unrepentant, entitled, and privileged corporate rapaciousness. She contends that these companies’ monetization of large amounts of data collected on users and algorithmic prioritization of advertisements and other content has led the public to understand that Facebook is not a neutral space for online interactions and Google is not a level playing ground for information. Similarly, she says, it’s dawning on users that Uber is not a neutral transactional company.

Uberland’s central weakness is Rosenblat’s credulity around Uber’s seemingly inevitable rewriting of employment—that a precarious, algorithmically determined pseudoemployment is the future for most workers. She acknowledges that app-based workers accounted for just 0.5% of all workers in the United States in 2015. Even if one assumes remarkable growth in app-based work, it would still account for only a small percentage of the workforce for the foreseeable future.

There is also the question of whether Uber’s model is so very new. We wish Rosenblat’s painstaking documentation of Uber’s algorithm and business strategy extended to a comparative analysis of privatization and deregulation in industries such as trucking, water and power, and charter schools. This framework would allow her to place Uber and other tech companies alongside their traditional counterparts, whose free-market ideology insists that the private sector can outperform the public sector at providing municipal services. It would help in providing understanding of whether the gig economy is truly novel, or simply a gilded repackaging of ongoing exploitative and profitable strategies such as deregulation, privatization, and misclassification of employee status.